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Published on: July 1, 2015
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Large-scale interactions in predictive processing: oscillatory versus transient dynamics
Martin Vinck1, Cem Uran1, Jarrod R Dowdall2
1Ernst Strüngmann Institute (ESI) for Neuroscience, in Cooperation with the Max Planck Society, 60528 Frankfurt am Main, Germany; Donders Centre for Neuroscience, Department of Neurophysics, Radboud University, 6525 Nijmegen, The Netherlands.
Trends in Cognitive Sciences
|October 18, 2024
Summary
Neural dynamics, including aperiodic transients and oscillations, play distinct roles in sensory inference. Oscillations may stabilize representations, while transients facilitate rapid inference, challenging classic predictive coding theories.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Cognitive Science
Background:
- Sensory inference relies on interactions between feedforward and feedback pathways.
- Neural dynamics, specifically aperiodic transients and oscillations, are proposed to mediate these interactions.
- Classic hierarchical predictive coding (HPC) offers a framework for understanding these processes.
Purpose of the Study:
- To explore the distinct contributions of aperiodic transients and oscillations to sensory inference.
- To evaluate the role of gamma and alpha/beta rhythms in classic HPC.
- To propose alternative models for neural dynamics in predictive processing.
Main Methods:
- Theoretical evaluation of existing models.
- Formulation of alternative functional models for neural dynamics.
- Discussion of biologically plausible alternatives to classic HPC.
Main Results:
- Critique of the theory assigning specific rhythms (gamma, alpha/beta) to feedforward and feedback communication in HPC.
- Proposal of a model where aperiodic transients support rapid inference, and oscillations stabilize representations and aid plasticity.
- Explanation of oscillation dependence on predictability via dendritic HPC.
Conclusions:
- Aperiodic transients and oscillations have differentiated roles in sensory inference and predictive processing.
- Alternative models offer a more nuanced understanding of neural dynamics beyond classic HPC.
- Dendritic HPC provides a potential explanation for the predictability dependence of oscillations.
